Background: Periprosthetic fracture (PPF) is a troublesome complication as it utilizes substantial healthcare resources. Recent studies about the epidemiology of PPF after total knee arthroplasty (TKA) are still lacking, and there is limited national-level analysis focusing on the comorbid chronic conditions as risk factors of PPF. This study used national registry data from South Korea and aimed to investigate the epidemiology of PPF following TKA between 2010 and 2020 and identify which comorbidities contributed to the risk of PPF. Methods: Using Health Insurance Review and Assessment (HIRA) service data in South Korea, the incidence of PPF after TKA between 2010 and 2020 was evaluated and stratified by age and sex. Medical comorbidities were evaluated as possible risk factors for PPF using Cox regression analysis. Results: PPF occurred in 14,429 patients, accounting for 2.37% of total TKA patients. The prevalence of PPF by sex was 2.50% in women and 1.64% in men. The PPF rate was 2.82% in under 60 years, 2.25% in 60 to 69 years, 2.42% in 70 to 79 years, 2.29% in 80 to 89 years, and 2.12% in over 90 years. Among 17 analyzed comorbidities, 11 were found to be associated with PPF after TKA. Severe liver disease (hazard ratio [HR], 1.303), hemiplegia (HR, 1.244), and dementia (HR, 1.206) were the top 3 risk factors.Although osteoporosis, pulmonary disease, peptic ulcer, and diabetes showed relatively low HRs than these top 3 factors, the incidence rates were higher. Conclusions PPF occurred in 2.37% of TKA patients in South Korea from 2010 to 2020. PPF rate was higher in women. To prevent PPF after TKA, proper patient management and education should be emphasized, particularly in patients with severe liver disease, hemiplegia, and dementia.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Patients with ectodermal dysplasia often have atrophied alveolar bone and an inadequate maxillomandibular relationship owing to congenital edentulism.Accurate implant placement that can overcomes anatomical limitations and orthognathic surgery to improve the maxillomandibular relationship is necessary for creating implant-supported prosthesis for these patients. Implant placement and provisional prosthesis fabrication before orthognathic surgery can provide critical fixed reference points and ensure accuracy during orthognathic surgery.In our patient, a digital system was used to design a surgical guide that considered the predictable position of the definitive prosthesis, allowing the placement of implants to overcome anatomical limitations and the creation of fixed reference points via the delivery of a provisional prosthesis for effective orthognathic surgery. The lack of compensation during orthognathic surgery was considered in the definitive prosthesis. As a result, a prosthesis with a minimal anterior cantilever was fabricated. This study aimed to determine the appropriate sequence of multidisciplinary collaborations that would, result in the best functional and aesthetic outcomes.

The impact of Streptococcus pneumoniae coinfection on coronavirus disease 2019 (COVID-19) prognosis remains uncertain. We conducted a retrospective analysis of patients hospitalized with COVID-19 who underwent a pneumococcal urinary antigen (PUA) test to assess its clinical utility. Results showed that PUA-positive patients required more oxygen support, high-flow nasal cannula, and dexamethasone compared to PUA-negative patients.Furthermore, the significantly higher incidence of a National Early Warning Score ≥5 in the PUA-positive group (P<0.001) suggests that a positive PUA test is associated with a severe disease course. However, no significant difference in mortality was observed between the two groups, and antibiotics were used in almost all patients (96.2%). While the PUA test may help guide antibiotic use in COVID-19 patients, its interpretation should be approached with caution.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Patients with ectodermal dysplasia often have atrophied alveolar bone and an inadequate maxillomandibular relationship owing to congenital edentulism.Accurate implant placement that can overcomes anatomical limitations and orthognathic surgery to improve the maxillomandibular relationship is necessary for creating implant-supported prosthesis for these patients. Implant placement and provisional prosthesis fabrication before orthognathic surgery can provide critical fixed reference points and ensure accuracy during orthognathic surgery.In our patient, a digital system was used to design a surgical guide that considered the predictable position of the definitive prosthesis, allowing the placement of implants to overcome anatomical limitations and the creation of fixed reference points via the delivery of a provisional prosthesis for effective orthognathic surgery. The lack of compensation during orthognathic surgery was considered in the definitive prosthesis. As a result, a prosthesis with a minimal anterior cantilever was fabricated. This study aimed to determine the appropriate sequence of multidisciplinary collaborations that would, result in the best functional and aesthetic outcomes.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.